# Paper Navigator

> Find and read academic papers (S2 + arXiv). Disambiguate ambiguous queries, search by keyword + citation graph + recommendations + snippets, judge relevance against an authored rubric, and read with L1/L2/L3 strategy. Trigger phrases: find papers, search papers, related work, citation analysis, recent advances, read this paper, baseline with code. Do NOT use for: survey reports (research-survey), idea generation (research-ideation), Related Work sections (paper-writing).

- Skill: `evoscientist/paper-navigator` (Agent Skill, multi-file: 30 files)
- Install (CLI): `npx skillmds@latest add evoscientist/paper-navigator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/evoscientist/paper-navigator/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: evoscientist (https://skillmd.com/u/evoscientist)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/evoscientist/paper-navigator

---


# Paper Navigator

Find and read academic papers. Route by **intent**, judge by **relevance**.

```
        User
         │
         ▼
   ┌── Router ──┐
   │            │
   ▼            ▼
 POINT      LIST/ITERATIVE
(1 paper)   (Probe + up to 3 paper rounds:
             R2 breadth / R3 deepen / R4 close)
```

The agent does relevance judgment — no LLM-as-judge is called, no numeric scoring. You author the rubric, you triage each paper, you rank by relevance.

## Setup

Scripts at `skills/paper-navigator/scripts/`. Run via `python skills/paper-navigator/scripts/<name>.py`.

arXiv access (`arxiv_monitor`, `scholar_search` fallback) uses the DeepXiv SDK: `pip install deepxiv-sdk`, then `deepxiv token` once to provision a **free** API token (saved to `~/.env`). The skill reads the token from `DEEPXIV_API_TOKEN`/`DEEPXIV_TOKEN` in the environment, or from `./.env` / `~/.env`.

| Env var | Used by | Notes |
|---|---|---|
| `S2_API_KEY` | All S2 scripts | Without it: `scholar_search` falls back to arXiv (via DeepXiv); `citation_traverse` / `recommend` / `snippet_search` are disabled |
| `DEEPXIV_API_TOKEN` | `arxiv_monitor`, `scholar_search` fallback | Get a free token: `deepxiv token` (writes `~/.env`). Also read from `DEEPXIV_TOKEN` and `./.env`/`~/.env`. ~10,000 req/day |
| `JINA_API_KEY` | `fetch_paper` | Free tier works without key |
| `GITHUB_TOKEN` | `github_search`, `find_code` | Higher rate limits |
| `PAPER_NAV_PAPERS_DIR` | `fetch_paper` full text | No default — set or pass `--metadata-only` |

Full env-var list: `references/env-vars.md`.

---

## Five Red Lines (always)

1. **Track history.** Don't re-run a query you already ran. Empty result → change angle, not synonyms.
2. **Search a gap, not a vibe.** Every query maps to one missing piece of information. No stacked-keyword bags.
3. **One query = one concept.** Split comparisons (`A vs B`), multi-property asks, and multi-year spans into separate calls.
4. **Never hallucinate.** Every fact (title, author, year, citation count, content) comes from a tool result.
5. **Quote-or-zero.** When you claim a paper meets a criterion, quote a ≤80-char span from its abstract / tldr / snippet. No quote → do not claim the paper meets that criterion. (This guards against hallucination; it does not drive a numeric score.)

---

## Router

| Branch | User signal | Cadence | Output |
|---|---|---|---|
| **POINT** | Title quoted, URL, arXiv/DOI/PMID/S2 ID, "read this paper" | 1 call | Paper Card |
| **LIST** (default) | "find papers about X", "is there a paper that …?", "papers satisfying A and B" | Probe + up to 3 rounds (R2/R3/R4) | Shortlist with per-criterion evidence |
| **ITERATIVE** | "survey of X", "30+ papers on Y", called from `research-survey` / `research-ideation` | Probe + up to 3 rounds (R2/R3/R4) | Ranked table (hand off to research-survey for the report) |

**Default to LIST when unsure.** Don't add `survey` / `review` to LIST queries — it down-ranks the canonical research papers the user wants.

**Output format follows the caller:** the Output column above is the structured form (skill callers). Direct user calls default to **Narrative** — see Step 6 "Output mode".

Ambiguous query (project nickname, codename, single capitalized word with zero hits) → run `scholar_search` exact + web/GitHub search first to resolve identifiers, then re-route.

---

## POINT branch (known paper)

| Input | Command | Output |
|---|---|---|
| URL | `python scripts/fetch_paper.py --url <URL>` | Paper Card + reading notes (see `references/reading-strategy.md` for L1/L2/L3) |
| Title quoted | `python scripts/match_paper_by_title.py --title "<title>"` (add `--fallback-search` for typos) | Paper Card |
| Bare ID (arXiv / DOI / S2 / CorpusId) | `python scripts/fetch_paper.py --paper-id <ID> --metadata-only` | Paper Card |

**Paper Card:**

```
📄 **<Title>**
Authors: <First Author> et al. | Year: <Y> | Venue: <V>
Citations: <N> | ID: <ArXiv:xxxx.xxxxx> | DOI: <...>
TLDR: <one sentence>
```

Stop here. Do not chain to citation expansion unless asked.

---

## LIST / ITERATIVE branch — 6 steps

### Step 1: Parse intent

State in one sentence: the **research object** (specific technique / concept) and the **constraints** (domain, task, recency, exclusions). Confirm the router branch. When the user gives only a bare noun (concept / model / algorithm / benchmark name) with no direction, default intent is "trace the lineage" — foundations, evolution, current state — not applications or a generic `survey`.

### Step 2: Author the RUBRIC (via `think_tool`)

Emit a structured block before any search. It persists across rounds and every later step references it.

```
RUBRIC for "<user query verbatim>"
Branch: LIST | ITERATIVE
Criteria (2–4, atomic; mark each [core] or [secondary]):
  C1 [core]     <what the paper MUST do/be — one sentence>
  C2 [core]     <...>
  C3 [secondary] <...>
Named entities to preserve verbatim: [<ent1>, <ent2>, ...]
Angle tags (3–5 sub-topic axes): [<tag1>, <tag2>, <tag3>]
Recency signal: [none | recency cue — defined in Step 3]
Disqualifiers: [<auto-reject if abstract shows this>]
```

Rules:
- **Criteria** atomic (one condition each), non-redundant. Mark each `[core]` (must-have) or `[secondary]` (nice-to-have) — this guides relevance ranking, no weights, no math.
- **Named entities** = proper-noun / technical-term anchors from the user's query. Every entity appears verbatim in ≥1 query across Probe + R2.
- **Angle tags** = sub-topic axes (`method`, `task`, `dataset`, `evaluation`, `domain`, …). No two queries in the same round share a tag.
- **Recency signal** = whether the user query carries a recency cue (see Step 3 recency trigger). Drives `--year-min` and the Step 6 recency tie-break.
- **Disqualifiers** = "specifically X, **not** Y" exclusions. Tripping a disqualifier → Irrelevant.

For ITERATIVE, criteria can be lighter (e.g. `covers topic` + `is canonical`); disqualifiers may be empty.

### Step 3: Search — Probe then Multi-round (Breadth → Deepen → Close)

Probe first to grasp the user's need comprehensively, then run up to **3 paper rounds** (R2 breadth / R3 deepen / R4 close). Decide round-by-round whether more rounds are needed (Step 5 gate) — **do NOT fix a round count**. There is no R5; the cap is 3 paper rounds.

**Probe (prerequisite — NOT counted in the 3 rounds, 2 parallel queries):** establish a comprehensive understanding of the need and lift named entities / angle gaps.
- `Q-broad` — canonical phrasing of the topic (angle: `general`)
- `Q-narrow` — a specific mechanism / sub-question / method (angle: tagged)

```bash
python scripts/scholar_search.py --query "<Q-broad>"  --limit 15 --sort-by relevance --output /tmp/pool.jsonl --append
python scripts/scholar_search.py --query "<Q-narrow>" --limit 15 --sort-by relevance --output /tmp/pool.jsonl --append
```

`--output --append` auto-dedupes by `paperId` across rounds (built into the script), so a paper found by two queries is written once. Read `/tmp/pool.jsonl` to inspect (Step 4 triage). From Probe titles + tldrs, lift:
- recurring **named entities** (algorithm / benchmark / dataset / model names),
- **angle gaps** (Step-2 tags not seen),
- vocabulary from **adjacent communities**.

**R2 — Breadth (default — skipped only via the Step 5 early exit; 2–3 parallel queries).** Broad academic queries on the core object + canonical terms lifted from Probe. The main axis is the user's core object + canonical mapping; Probe only supplies high-confidence supplements.

**R3 — Targeted deepening (optional, when Step 5 gate says CONTINUE, 2–3 parallel queries).** Driven by the gap→strategy map below — fill the specific gap Step 4 triage exposed.

**R4 — Closing (optional, 2–3 parallel queries).** Fill whatever key gap remains after R3: a missing representative work, a strong baseline, a counter-example, or an uncovered sub-direction.

**Gap→strategy map** (R3/R4 are driven by the Step-4 triage gap, not vibes):

| Gap surfaced by Step 4 triage | R3/R4 strategy |
|---|---|
| Foundational work drowned by recent papers | `--year-max`, search the original mechanism / early terminology |
| User wants SOTA / frontier, or R2 skewed old | `--year-min` last 2 years |
| Thin single-source evidence | swap terminology / team / benchmark for multi-source corroboration |
| Incomplete A-vs-B comparison | separately fill A, B, and an upper-topic query (no `survey`/`review` terms) |
| Contradictory findings | verification query, prefer authoritative venue / high-cite / direct experiment |

**Per-query rules:**
- 3–6 words preferred (English academic terms); <3 over-recalls, >6 dilutes ranking.
- Use academic terms (`mechanism`, `benchmark`); no `how it works` phrasing.
- **Do NOT add `survey` / `review` / `tutorial` terms** — they bias results toward review papers and crowd out the research papers the user wants. Only use them when the user explicitly asks for a survey/review.
- Bare entity names; no `paper` / `pdf` / `arxiv` / `original`.
- Split comparisons / multi-property; if short of 2–3 queries, fill with upper-topic or representative method.
- Time intent goes into `--year-min/max` params, never year words in the query.
- Forbidden: `"…"`, `(..)`, `OR`, `AND`, `|`, `site:`, `filetype:`.
- No two queries in one round may share >60% of content tokens (after stop-words).

**Recency trigger (query layer).** When the user query contains a recency signal (最新 / 近年 / 近期 / 近两年 / 前沿 / SOTA / latest / recent / state-of-the-art), set `--year-min` to **last 2 years** from R2 onward — Jan 1 of the year before the current system year (e.g. system date 2026-08 → `--year-min 2025`). Do not extend to 3–4 years.

**Without `S2_API_KEY`:** swap `scholar_search` for `arxiv_monitor --keywords "<variant>" --match-mode flexible --days 3650`.

**Citation expansion** (ITERATIVE, or LIST after ≥3 All-core/Partial seeds):
```bash
python scripts/citation_traverse.py --paper-id <SEED> --direction co-citation --limit 15 --output /tmp/pool.jsonl --append
python scripts/citation_traverse.py --paper-id <SEED> --direction forward --limit 20 --min-citations 20 --year-min 2022 --output /tmp/pool.jsonl --append
python scripts/recommend.py --positive <SEED1>,<SEED2> --limit 15 --output /tmp/pool.jsonl --append
```

### Step 4: Triage — All-core / Partial / Irrelevant

After every round, classify each new paper and stamp a **per-criterion mask** (✓ / ~ / ✗) over the RUBRIC — **no numeric scoring**. The mask is what makes conjunctive queries ("papers satisfying A and B") terminate correctly: only a paper with `✓` on **every [core] criterion** is `All-core`. Emit a `think_tool` block:

```
TRIAGE round=<n>  query="<q>"
  All-core   (k): <paperId> "<title-≤60>" Y=<year> · [C1✓ C2✓ (C3~)]   every [core] ✓
                 C1: "<≤80-char quote>"
                 C2: "<≤80-char quote>"
  Partial    (k): <paperId> "<title>" Y=<year> · [C1✓ C2✗]              some [core] ✓
                 C1: "<≤80-char quote>"
  Irrelevant (k): <paperId> "<title>"                                   no [core] ✓, or trips disqualifier — drop
```

| Tier | Mask | Quotes |
|---|---|---|
| `All-core` | every [core] criterion `✓` (no `✗` on any [core]) | one ≤80-char quote per [core] criterion |
| `Partial` | at least one [core] `✓`, but some [core] `✗`/`~`; or only [secondary] support | one quote per `✓` [core] criterion |
| `Irrelevant` | no [core] `✓`, or trips a disqualifier | none — drop from later rounds |

`✓` = abstract/tldr clearly supports. `~` = partial / inferable. `✗` = no support or contradicts. [secondary] criteria don't set the tier but still get a mask symbol.

Rules:
1. **Dedup across rounds** by `paperId` first, then normalised title. Keep the stronger mask.
2. **Disqualifier check** beats all other matches → Irrelevant.
3. **Re-diagnose gaps:** note any [core] criterion with 0 `✓` across All-core+Partial, and any angle tag with 0 All-core/Partial → that's the next refine target (feeds the Step 3 gap→strategy map).
4. **No fabrication:** missing abstract → stamp `~`, do not infer from training data.

The per-criterion quotes collected here are exactly what the Step 6 Rank-1 bar and the structured LIST template cite — do not skip them.

**Snippet upgrade** for borderline papers (abstract silent on a [core] criterion): batch-fetch real body text:
```bash
python scripts/snippet_search.py --query "<criterion phrase>" \
  --paper-ids "CorpusId:1,CorpusId:2,..." --limit 50
```

### Step 5: Saturation Gate

After **Probe and each round**, decide CONTINUE vs STOP by **whether key gaps remain** — not by counting papers.

**Early exit after Probe (single-recommendation / conjunctive queries only, K=1–2):** for "is there a paper that …?", "recommend a paper", "what's the canonical X", or conjunctive "papers satisfying A **and** B" queries, if an `All-core` paper already covers every [core] criterion, **STOP** without running further rounds. This preserves the fast path where one probe hit settles a POINT-like query (≈2 queries), which matters under keyless S2 rate limits. For broader question shapes ("find papers about …" K=3–5, or 30+-paper ITERATIVE), do **not** use this early exit — apply the full STOP conditions below.

**STOP when ALL hold:**
- ≥1 `All-core` paper exists — a *single* paper with `✓` on every [core] criterion. This is required so conjunctive queries like "papers satisfying A **and** B" can't pass on two different papers that each cover only one side, AND
- every angle tag has ≥1 All-core/Partial paper, AND
- no key claim rests on a single source,
- OR further rounds stop surfacing anything new (empty recall / all duplicates).

**CONTINUE to the next round (R3 / R4) otherwise**, driven by the gap→strategy map:
- 0 `All-core` papers → fill the [core] criterion still `✗`.
- An angle tag has 0 All-core/Partial → open that angle.
- A key claim rests on a single source → multi-source corroboration.

**Re-decompose** (rubric is wrong) if R2 returns 0 All-core AND 0 Partial across the board: report the strongest Partial candidate(s) + ask the user to relax a criterion.

**Round caps:** LIST and ITERATIVE up to 3 paper rounds (R2/R3/R4). POINT is a single fetch (no multi-round). If still not saturated at the cap, go to Step 6 and report which criteria / angle tags were not covered.

**The gate is mechanical about gaps** — do not skip rounds because "the results look right"; do not run extra rounds once the STOP conditions hold. The single-All-core-suffices shortcut applies only to the K=1–2 early exit above, not to broader queries.

### Step 6: Rank and Output

**Gather:** every All-core and Partial paper from across all rounds (dedup by `paperId`). Drop Irrelevant.

**Rank by relevance — the model judges, no numeric score.** Order the gathered papers by how directly each answers the user's question: All-core before Partial; a paper satisfying every [core] criterion ranks above one satisfying only some. This is a judgment call, not a formula — **do not compute a weighted_total**.

**Recency-aware tie-break.** When the RUBRIC flagged a recency signal (cues defined in Step 3), break ties / near-ties in favor of the more recent paper (`year` DESC), applied **after** relevance. Recency rides behind relevance, never ahead of it.

**K (soft ceiling — prevents pool-dumping):**

| Question shape | K |
|---|---|
| "Exactly N papers" | N |
| "Is there a paper that …?" / "Recommend a paper" | 1–2 (bold top-1) |
| "Find papers about …" | 3–5 |
| "Survey of …" / ITERATIVE | ≤ 10 (soft cap) + 1–2 surveys if the user explicitly asked for them |

K is a soft guide, not a formula. For broad "survey / categorize the field" queries the dominant failure mode is dumping the whole accumulated pool (often 30–50 papers, most off-criterion) into the output, burying the few on-criterion papers. Rank by relevance, keep the top K, and move the rest to an **"Also relevant (not ranked)"** list — never pad the ranked list with weak papers to reach a count.

**Rank-1 quality bar.** For single-recommendation queries ("is there a paper that …?", "recommend a paper", "what's the canonical X") the bolded top-1 must be an **All-core** paper — clearly satisfying every [core] criterion with a quote. Rank 1 carries disproportionate weight in user perception; fronting a Partial paper at top-1 reads as a confident wrong answer. If no paper clears the bar, lead with "No fully-matching paper found" and present the strongest near-miss honestly with its gaps.

If no All-core paper survives after the round cap, report "no fully-matching paper found", list strongest Partial candidates + their gaps, stop.

**Output mode (caller inference).** Pick the format by what the request demands, not by an explicit flag:
- **Structured** (formats below) — when the request demands machine-consumable output: a ranked list/table, per-criterion evidence, or hand-off to a downstream skill. Default for skill callers (`research-survey`, `research-ideation`, `paper-writing`, `experiment-pipeline`).
- **Narrative** (see "Narrative output" below) — the default for direct user calls: natural-language questions with no structured-output demand.

**Structured output formats (skill callers):**

LIST (shortlist with evidence):
```
**Top matches:**
- **<paperId>** "<Title>" — <Authors> et al., <Year>, <Venue>, cited by <N>. <URL>
  - C1 [core]: "<quote>"
  - C2 [core]: "<quote>"
  - C3 [secondary]: "<quote>"

**May also be relevant:**
- <paperId> "<Title>" — <Authors> et al., <Year>, cited by <N>. <URL> (Partial: only C3)
```

ITERATIVE (ranked table):
```
| # | Title | Authors | Year | Venue | Cited by | Link |
|---|-------|---------|------|-------|----------|------|
| 1 | …    | … et al. | 2024 | NeurIPS | 1234 | <URL> |
```

POINT: Paper Card (above).

**Narrative output (direct user callers).**

Deliver structured knowledge, not a search trace. Strip process words before output (`Probe`, `R2/R3/R4`, `All-core/Partial`, "rounds done") unless the user explicitly asks for a trace.

- **Information-first, not list-first.** Unless the user only wants a paper list, do not collapse the answer into "title + one-line contribution". Build the cognitive structure the user needs (timeline / topic grouping / comparison / mechanism breakdown / evidence verification / reading path), then place papers into it as evidence nodes.
- **One main form + 2–3 auxiliary forms.** The main form carries the answer's logic (timeline, topic grouping, comparison, mechanism breakdown, evidence grading, reading path, mini-survey); 2–3 auxiliary forms (paper card, table, evidence grading, annotated bibliography, reader payoff) aid readability. Do not stack every form.
- **Intent → form (condensed):** latest/SOTA → status-judgment + table; origin/foundation → timeline + source-paper analysis; A-vs-B → conclusion + dimension comparison; mechanism → mechanism breakdown + evidence interleaving; benchmark/data → verification + evidence table; landscape → topic grouping + reading path.
- **Citation rules:** a core paper shows **Title (Venue Year)** on first mention (never author-only like `Zhang et al.`); every cited paper carries its returned `[N]`. **Tables MAY contain `[N]` markers.** At the end of the answer, list every cited paper by number in **IEEE style** (with citation count appended): `[N] A. Author et al., "Title," Venue, Year, cited by N. [Online]. Available: URL`. IEEE rules: author names as `Initial. Surname` (e.g. `A. Vaswani`); join multiple authors with commas and `and`; ≥3 authors → `A. Firstauthor et al.`; title in double quotes; then `Venue, Year`; append `cited by N` (the tool's citationCount); end with `[Online]. Available: URL` for the link. Omit a field only if the tool genuinely did not return it (never fabricate); authors and citation count must appear whenever returned.
- **Length:** single-point 600–1200 words; comparison/retrieval 1200–2500; landscape/timeline 2000–3500. Do not sacrifice evidence structure for brevity.
- **Quote-or-zero still applies** — every claim a paper is used to support is still backed by a ≤80-char quote (Red Line 5, anti-hallucination); the narrative just renders it as `[N]` instead of showing the raw quote.

**Pre-output checklist (mandatory).** Before emitting the answer, verify each box.

- [ ] **Pool gathered** from every round's triage, deduped by `paperId`, Irrelevant excluded.
- [ ] **Ranked by relevance** (judgment, not a numeric score) — All-core before Partial.
- [ ] **Recency tie-break applied** when a recency signal is present (`year` DESC after relevance).
- [ ] **Rank-1 clears the bar** for single-recommendation queries (clearly satisfies every [core] criterion with a quote) — or you've reported "No fully-matching paper found".
- [ ] **Every cited paper has ≥1 supporting quote** for the claim it's used for (quote-or-zero, Red Line 5 — anti-hallucination).
- [ ] **Output ≤ K** (soft cap); surplus relevant papers sit in "Also relevant (not ranked)", not the ranked list.
- [ ] **Narrative mode only** — every cited paper appears in the end-of-answer numbered reference list in **IEEE style with authors + citation count** (`[N] A. Author et al., "Title," Venue, Year, cited by N. [Online]. Available: URL`), and the body strips process words (`Probe` / `R2` / `All-core/Partial`).

If any box is unchecked, return to Step 6 — do not output.

---

## Tool Cheat Sheet

| Need | Script | Notes |
|---|---|---|
| Keyword search | `scholar_search.py` | S2 → arXiv fallback on missing key / 429 |
| Title → record | `match_paper_by_title.py` | S2 exact-match; `--fallback-search` for typos |
| Citation graph | `citation_traverse.py` | `--direction forward/backward/co-citation`; `--min-citations`; `--year-min/max`; `--smart-sort`; `--enrich` |
| Similar papers | `recommend.py` | seed-based; `--per-seed` for diverse seeds |
| Author papers | `author_search.py` | `--sort-by year/citations` |
| New arXiv | `arxiv_monitor.py` | `--categories cs.CL` or `--keywords "x,y" --match-mode flexible` |
| Trending | `trending.py` | citation velocity |
| Body-text snippets | `snippet_search.py` | `--paper-ids c1,c2,c3 --limit 50` (1 call, not N) |
| Fetch full text | `fetch_paper.py` | Saves to `$PAPER_NAV_PAPERS_DIR/<id>.md`; stdout truncated to 2000 chars |
| Code repo (known paper) | `find_code.py --arxiv-id <ID>` | Official repo lookup |
| Code repo (unpublished) | `github_search.py` | When no arXiv ID exists |
| HF leaderboard / SOTA | `sota.py` | sorted by downloads |
| HF datasets | `dataset_search.py` | Query short-name (`imdb`, `sst2`), not task description |
| Saturation gate (optional) | `saturation.py` | JSONL log of per-round yields; `estimate` returns STOP/CONTINUE |

All discovery scripts: `--limit N`, `--json`, `--output FILE`, `--append`; accept S2 / arXiv / DOI / CorpusId IDs. `--output --append` auto-dedupes by `paperId` across rounds (within-batch + cross-file), so the pool stays clean.

---

## Rate limits

| API | Without key | With key |
|---|---|---|
| Semantic Scholar | ~1 req / 3s, no parallel | 100 req/min, parallel OK |
| arXiv | 1 req / 3s (courtesy) | N/A |
| GitHub | 10 req/min | 5,000 req/hr |
| HuggingFace | 500 req / 300s | Higher with `HF_TOKEN` |

Global S2 pacer + circuit breaker (5 failures → 60s cooldown). Retries: 3s / 6s / 12s / 24s / 48s.

Without `S2_API_KEY`: use `scholar_search` (arXiv fallback) + `arxiv_monitor`. Skip `citation_traverse` / `recommend` / `snippet_search` — they're S2-only; do not retry.

---

## References

| File | Read when |
|---|---|
| `references/env-vars.md` | Setting environment variables |
| `references/search-principles.md` | Per-query rules, gap diagnosis, rate-limit recovery |
| `references/iterative-collection.md` | ITERATIVE corpus collection (30+ papers): phase mapping, citation expansion, escape hatches |
| `references/disambiguation.md` | Query is a project nickname / codename |
| `references/reading-strategy.md` | L1 / L2 / L3 reading framework |
| `references/api-reference.md` | S2 / arXiv / Jina / HF / GitHub endpoint details |
| `references/arxiv-categories.md` | arXiv category codes |
| `references/output-formats.md` | Baseline / Disambiguation / Reading-Notes / Citation-Graph templates |

References are self-contained. Don't chain between them — return here to re-route.

---

## Hand off to

| Goal | Skill |
|---|---|
| Survey report | `research-survey` |
| Idea generation | `research-ideation` |
| Related Work section | `paper-writing` |
| Baseline + experiment | `experiment-pipeline` |